Advanced Remote Sensing: Lab 7: Change Detection

Goals and Background

The main goal of lab 7 was to gain knowledge in change detection with land use/land cover. Digital change detection is very important to remote sensing as it shows environmental and socioeconomic progression or even digression over periods of time. For this lab there were two visualization methods used to look at land cover change over time. The first was a write function method which highlights change from two different images. The other was a From-To change method which showed a specific land cover change from one to the other. Another part of land cover change is to find the percent in change which was demonstrated with an excel table in the following methods.

Methodology

The first part of the lab focused on change detection using Write Function Memory Insertion. The basis of write function memory is using near-infrared bands from two dates to highlight changes in land. To accomplish this change detection method 3 images are needed. The area for this lab was Eau Claire and surrounding counties. For this lab the 3 images were used an August 2011 image from the red band, band 3, called ec_envs_2011b3.img the other 2 images were from the same area from 1991 and were also the same. These 2 images were ec_envs_1991_b4.img and ec_envs_1991_b4copy.img. These 3 images are layer stacked in ERDAS Imagine software and saved as a new image. This image being ec_envs91-11chg.img. To show the change the image bands need to be switched. This is found under the Multispectral tab. The 2011 image should be in the Red color gun and the 1991 images should be in the Green and Blue color guns. The new image now shows changes of land by highlighting them as red. The image shows a lot of change in urban areas and this can be explained by the ever-changing environment of cities and populated areas.

The second part of the lab was using a different change detection method. The From-To change shows the change of an area and explains what it changed to. The area of this method is the Milwaukee Metropolitan Statistical Area and the years are 2001 and 2006. The first step was to look at the quantitative data and change of the area. This step is done in Microsoft Excel. Two columns were created for each image. The first column was the class of the LULC image and the second column was the histogram in square meters. To convert the histogram values into meters multiply the histogram by 900. This gets square meters for the value. The next step was to convert square meters into hectares. All that is done here is multiplying the square meters by 0.0001. Once all the conversions are done for both 2001 and 2006 finding the percent change for the Milwaukee Statistical Area needs to be done. Percent change is calculated by subtracting the 2006 hectares from the 2001 hectares, using the increase divide by the 2001 hectares and multiply by 100. This will give the percent change from 2001-2006, which there can be positive or negative change.

	2001	2006
	Hectares	Hectares
Water	15182.91	15272.82	59%
Open Space	32644.53	36899.1	13%
Urban/built up	89209.89	92993.76	4%
Bare Soil	1177.92	1456.2	23%
Forest	48051	46895.31	-2%
Shrub	5936.31	5431.77	-8%
Agriculture	158188.41	151771.23	-4%
Wetland	44820	44490.78	-0.70%
Total	395210.97	379938.15

The final part of the lab was to create an LULC map using a model. This model was created by Dr. Cyril Wilson and a colleague at Indiana State University and is called the Wilson-Lula algorithm. The equation for the model is as follows

ΔLUC = [IM1(v1….vn) – vt = set{0,1a}] [IM2(v1….vn) – vt = set{0,1b}] = 1a & 1b.

ΔLUC is the From-To change class, IM1 is the image for the first date, IM2 is the second image from the second date, v1….vn are the class values. Vt are classes not used for a sub-model, set{0,1} mask the classes not used in this model but highlights ones that are used, 1_a is from the pixel value of classes, and 1_b is to the pixel value of the classes. The model for this uses two raster objects, 10 function objects, 10 raster objects, another 5 function objects, and another 5 raster objects. The 2001 and 2006 images are put into the top two raster objects. The functions use the algorithm above. In the two sets of functions is where the from-to change occurs. The first function of the two is the original class from 2001 and the second function is what it will change to. The functions will be set like this, but change pertaining to their from-to classes: EITHER 1 IF ($n1_milwauke_2001==7) OR 0 OTHERWISE. Under the functions are raster objects. These rasters are temporary rasters and should be sets as integer. Under the second raster sets is the second functions. These functions will show the areas of change by showcasing fucntions similar to $n13_memory & $n14_memory. The final raster is the raster output. Each of these five outputs are named for their from-to change classes. Once these rasters are saved they are displayed on a map showcasing the change.